Statistics and Data Science
Description
This book constitutes the proceedings of the Research School on Statistics and Data Science, RSSDS 2019, held in Melbourne, VIC, Australia, in July 2019.
The 11 papers presented in this book were carefully reviewed and selected from 23 submissions. The volume also contains 7 invited talks. The workshop brought together academics, researchers, and industry practitioners of statistics and data science, to discuss numerous advances in the disciplines and their impact on the sciences and society. The topics covered are data analysis, data science, data mining, data visualization, bioinformatics, machine learning, neural networks, statistics, and probability.
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Content
Invited Papers.- Symbolic Formulae for Linear Mixed Models.- code::proof: Prepare for most weather conditions.- Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models.- Flexible Modelling via Multivariate Skew Distributions.- Estimating occupancy and fitting models with the two-stage approach.- Component elimination strategies for mixtures of multiple scale distributions.- An introduction to approximate Bayesian computation.- Contributing Papers.- Truth, Proof, and Reproducibility: There's no counter-attack for the codeless.- On Adaptive Gauss-Hermite Quadrature for Estimation in GLMM's.- Deep learning with periodic features and applications in particle physics.- Copula Modelling of Nurses' Agitation-Sedation Rating of ICU Patients.- Predicting the whole distribution with methods for depth data analysis demonstrated on a colorectal cancer treatment study.- Resilient and Deep Network for Internet of Things (IoT) Malware Detection.- Prediction of Neurological Deterioration of Patients with Mild Traumatic Brain Injury using Machine Learning.- Spherical data handling and analysis with R package rcosmo.- On the Parameter Estimation in the Schwartz-Smith's Two-Factor Model.- Interval estimators for inequality measures using grouped data.- Exact model averaged tail area confidence intervals.